Predictions made in relation to a spatial map may be complex and include several variables. However, there are many business, municipal and investment decision-makers that would like to grasp valuable information from glancing at the output of predictive computing methods. For example, there is value in gaining insight into a particular geographical location for predicting optimal store, hospital, fire house, and other locations. There is also value in predicting localized real-estate value trends, predicting voter turnout in a political campaign, and gauging the expected efficacy of an advertising campaign.
In the past a data analyst had to investigate numerous sources of non-homogenous data. Intuition, in addition to analytical methods, has been used to arrive at predictions about any given geographical location. The resulting prediction may have been presented to decision-makers in the form a spreadsheet or compilation of computer print-outs. Some decision makers have, at times, been overwhelmed by the presentation of disparate data in a non-user friendly form. The inability of a decision-maker to quickly parse though numerous predictions could render such predictions virtually valueless.